Initially proposed for speech recognition [1]}, an ARHMM is a particular
dynamic Bayesian network that draws benefits of an AR model and HMM.
A standard and sound learning procedure of this model based on maximum likelihood (ML) allows parameters
to be estimated iteratively and simultaneously.
This paper suggests to use ARHMM for fault detection and prognostics of equipments based on sensors' data.
A modification of the learning procedure is also proposed to enable one to add prior knowledge on the latent structure.
This modification allows, in some way, to decrease the attachment of the model to the data that can be observed in practice
in various probabilistic models learned with the ML approach.
